ARIC™ for Payment Fraud Prevention
Fighting account takeover and scams in real time
Combine data from a variety of sources, including device ID, customer behavior, and cross-channel transactions in a single solution, protecting your customers from payments fraud while minimizing friction and reducing the need for manual intervention.
Understand behavioral data across all channels:
ARIC Transaction Monitoring offers best in class fraud prevention across each channel but also holistically allows you to spot complex fraud such as “pivoting fraud”.
Integrate multiple data feeds:
Featurespace customers are able to input over 80 data feeds to a single ARIC Fraud Hub, including fraud scores from third parties, call centre, card, cheque, ACH, and application data.
Protect your business and your customers with ARIC Fraud Hub
- Real-time detection of fraud using machine learning and Adaptive Behavioral Analytics
- Use unique Adaptive Behavioral Biometrics to monitor the whole online banking session and stop fraud before a payment is even attempted
- Works on all payment types (Faster Payments, BACS, Zelle, ACH, SWIFT, API and more).
- Automatic detection of both new and existing types of:
- Account Takeover
- Credential Stuffing
Supporting an Enhanced Customer Journey
Today’s customers expect a seamless and smooth payments experience from their bank or financial institution.
Our Adaptive Behavioral Biometrics solution maps behavioral data on websites and mobile apps to detect Account Takeover and Phishing Attacks, all in real time using machine learning.
Crucially the profiles are so accurate that phishing and malware activity can be spotted even if the session is generated by a genuine customer.
Effective risk management, including fraud prevention, is critical to retaining long-term growth and profitability. However, this tier 1 UK bank was facing an increased volume of fraud attacks against its SME business and retail customers.
Because the fraud attacks involved new fraud techniques such as malware, vishing, and smishing, traditional rules-based approaches were generating a high volume of alerts and struggling to fully mitigate the fraud problem. This resulted in an increase in operational expenditure and increased fraud losses.
of fraud value identified before payment attempted using in-session behavioral data
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